add mistral 12b recipe for 24.09

Signed-off-by: dimapihtar <dpihtar@gmail.com>

nemo/collections/llm/__init__.py

@@ -70,6 +70,7 @@
     LlamaModel,
     MaskedTokenLossReduction,
     MistralConfig7B,
+    MistralNeMoConfig12B,
     MistralModel,
     MixtralConfig8x3B,
     MixtralConfig8x7B,
@@ -118,6 +119,7 @@
     "gpt_data_step",
     "gpt_forward_step",
     "MaskedTokenLossReduction",
+    "MistralNeMoConfig12B",
     "MistralConfig7B",
     "MistralModel",
     "MixtralConfig8x3B",

nemo/collections/llm/gpt/model/__init__.py

@@ -53,7 +53,7 @@
     LlamaConfig,
     LlamaModel,
 )
-from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel
+from nemo.collections.llm.gpt.model.mistral import MistralConfig7B, MistralModel, MistralNeMoConfig12B
 from nemo.collections.llm.gpt.model.mixtral import (
     MixtralConfig8x3B,
     MixtralConfig8x7B,

nemo/collections/llm/gpt/model/mistral.py

@@ -58,7 +58,7 @@ class MistralConfig7B(GPTConfig):
 
 
 @dataclass
-class MistralNeMo2407Config12B(MistralConfig7B):
+class MistralNeMoConfig12B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-nemo/
     """
@@ -74,7 +74,7 @@ class MistralNeMo2407Config12B(MistralConfig7B):
 
 
 @dataclass
-class MistralNeMo2407Config123B(MistralConfig7B):
+class MistralNeMoConfig123B(MistralConfig7B):
     """
     https://mistral.ai/news/mistral-large-2407/
     """
@@ -324,7 +324,7 @@ def _export_qkv(ctx: io.TransformCTX, linear_qkv):
     target_key="decoder.layers.*.mlp.linear_fc1.weight",
 )
 def _import_linear_fc1(down, gate):
-    return torch.cat((down, gate), axis=0).float()
+    return torch.cat((down, gate), axis=0)
 
 
 @io.state_transform(

nemo/collections/llm/recipes/__init__.py

@@ -6,6 +6,8 @@
     llama3_70b_16k,
     llama3_70b_64k,
     mistral,
+    mistral_7b,
+    mistral_nemo_12b,
     mixtral_8x3b,
     mixtral_8x3b_16k,
     mixtral_8x3b_64k,
@@ -24,7 +26,8 @@
     "llama3_70b",
     "llama3_70b_16k",
     "llama3_70b_64k",
-    "mistral",
+    "mistral_7b",
+    "mistral_nemo_12b",
     "mixtral_8x3b",
     "mixtral_8x3b_16k",
     "mixtral_8x3b_64k",

nemo/collections/llm/recipes/mistral.py → nemo/collections/llm/recipes/mistral_7b.py

@@ -12,7 +12,7 @@
 from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
 from nemo.collections.llm.utils import Config, Partial, factory
 
-NAME = "mistral"
+NAME = "mistral_7b"
 
 
 @factory(name=NAME)

nemo/collections/llm/recipes/mistral_nemo_12b.py

@@ -0,0 +1,189 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Optional
+
+import nemo_run as run
+import pytorch_lightning as pl
+import torch
+from megatron.core.distributed import DistributedDataParallelConfig
+from pytorch_lightning.callbacks.callback import Callback
+
+from nemo import lightning as nl
+from nemo.collections.llm.api import finetune, pretrain
+from nemo.collections.llm.gpt.data.mock import MockDataModule
+from nemo.collections.llm.gpt.model.mistral import MistralModel, MistralNeMoConfig12B
+from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
+from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
+from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
+from nemo.utils.exp_manager import TimingCallback
+
+NAME = "mistral_nemo_base_12b"
+
+
+@run.cli.factory(name=NAME)
+def model() -> run.Config[pl.LightningModule]:
+    """
+    Factory function to create a Mistral-Nemo-Base-12B model configuration.
+    Returns:
+        run.Config[pl.LightningModule]: Configuration for the Mistral-Nemo-Base-12B model.
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain model=mistral_nemo_base_12b ...
+        Python API usage:
+            >>> model_config = model()
+            >>> print(model_config)
+    """
+    return run.Config(MistralModel, config=run.Config(MistralNeMoConfig12B))
+
+
+def trainer(
+    tensor_parallelism: int = 1,
+    pipeline_parallelism: int = 1,
+    pipeline_parallelism_type: Optional[torch.dtype] = None,
+    virtual_pipeline_parallelism: Optional[int] = None,
+    context_parallelism: int = 2,
+    sequence_parallelism: bool = False,
+    num_nodes: int = 1,
+    num_gpus_per_node: int = 8,
+    max_steps: int = 100,
+    callbacks: Optional[list[run.Config[Callback]]] = None,
+) -> run.Config[nl.Trainer]:
+    """
+    Configure the NeMo Lightning Trainer for Mistral-Nemo-Base-12B model.
+    This function sets up the distributed training strategy and other training parameters.
+    Args:
+        tensor_parallelism (int): Degree of tensor model parallelism.
+        pipeline_parallelism (int): Degree of pipeline model parallelism.
+        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.
+        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.
+        context_parallelism (int): Degree of context parallelism.
+        sequence_parallelism (bool): Whether to use sequence parallelism.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        max_steps (int): Maximum number of training steps.
+        callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.
+    Returns:
+        run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain trainer=mistral_nemo_base_12b ...
+        Python API usage:
+            >>> trainer_config = trainer(num_nodes=2, num_gpus_per_node=8)
+            >>> print(trainer_config)
+    """
+    strategy = run.Config(
+        nl.MegatronStrategy,
+        tensor_model_parallel_size=tensor_parallelism,
+        pipeline_model_parallel_size=pipeline_parallelism,
+        pipeline_dtype=pipeline_parallelism_type,
+        virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,
+        context_parallel_size=context_parallelism,
+        sequence_parallel=sequence_parallelism,
+        gradient_as_bucket_view=True,
+        ckpt_include_optimizer=True,
+        ckpt_async_save=True,
+        ckpt_parallel_load=True,
+        ddp=run.Config(
+            DistributedDataParallelConfig,
+            check_for_nan_in_grad=True,
+            grad_reduce_in_fp32=True,
+            overlap_grad_reduce=True,
+            overlap_param_gather=True,
+        ),
+    )
+
+    trainer = run.Config(
+        nl.Trainer,
+        accelerator="gpu",
+        accumulate_grad_batches=1,
+        callbacks=callbacks,
+        devices=num_gpus_per_node,
+        gradient_clip_val=1.0,
+        limit_test_batches=50,
+        limit_val_batches=32,
+        log_every_n_steps=10,
+        max_steps=max_steps,
+        num_nodes=num_nodes,
+        plugins=bf16_mixed(),
+        strategy=strategy,
+        use_distributed_sampler=False,
+        val_check_interval=2000,
+    )
+
+    return trainer
+
+
+@run.cli.factory(target=pretrain, name=NAME)
+def pretrain_recipe(
+    dir: Optional[str] = None, name: str = "default", num_nodes: int = 1, num_gpus_per_node: int = 8, fn=pretrain
+) -> run.Partial:
+    """
+    Create a pre-training recipe for Mistral-Nemo-Base-12B model.
+    This function sets up a complete configuration for pre-training, including
+    model, trainer, data, logging, optimization, and resumption settings.
+    Args:
+        dir (Optional[str]): Directory for saving logs and checkpoints.
+        name (str): Name of the pre-training run.
+        num_nodes (int): Number of compute nodes to use.
+        num_gpus_per_node (int): Number of GPUs per node.
+        fn (Callable): The pre-training function to use.
+    Returns:
+        run.Partial: Partial configuration for pre-training.
+    Examples:
+        CLI usage:
+            $ nemo llm pretrain --factory mistral_nemo_base_12b
+            $ nemo llm pretrain --factory "mistral_nemo_base_12b(num_nodes=2, name='my_mistral_pretrain')"
+        Python API usage:
+            >>> recipe = pretrain_recipe(name="mistral_pretrain", num_nodes=2)
+            >>> print(recipe)
+    """
+    return run.Partial(
+        fn,
+        model=model(),
+        trainer=trainer(
+            tensor_parallelism=2,
+            pipeline_parallelism=1,
+            pipeline_parallelism_type=None,
+            virtual_pipeline_parallelism=None,
+            context_parallelism=2,
+            sequence_parallelism=False,
+            num_nodes=num_nodes,
+            num_gpus_per_node=num_gpus_per_node,
+            callbacks=[run.Config(TimingCallback)],
+        ),
+        data=run.Config(MockDataModule, seq_length=4096, global_batch_size=512, micro_batch_size=1),
+        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
+        optim=distributed_fused_adam_with_cosine_annealing(max_lr=3e-4),
+        resume=default_resume(),
+    )
+
+
+@run.cli.factory(name=NAME + "_hf")
+def hf_resume() -> run.Config[nl.AutoResume]:
+    """
+    Configure automatic resumption from a Hugging Face checkpoint for Mistral-Nemo-Base-12B model.
+    This function sets up the configuration to resume training from a pre-trained
+    Hugging Face model checkpoint.
+    More info about the model can be found at: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
+    Returns:
+        run.Config[nl.AutoResume]: Configuration for resuming from HuggingFace checkpoint.
+    Note:
+        This is particularly useful for fine-tuning scenarios where you want to
+        start from the pre-trained Mistral-Nemo-Base-12B model.
+    """
+    return run.Config(
+        nl.AutoResume, restore_config=run.Config(nl.RestoreConfig, path="hf://mistralai/Mistral-Nemo-Base-2407")
+    )